nep-net New Economics Papers
on Network Economics
Issue of 2018‒04‒23
six papers chosen by
Pedro CL Souza
Pontifícia Universidade Católica do Rio de Janeiro

  1. Effectiveness of Connected Legislators By Marco Battaglini; Valerio Leone Sciabolazza; Eleonora Patacchini
  2. Who Owned Citibank? Familiarity Bias and Business Network Influences on Stock Purchases, 1925-1929 By Charles W. Calomiris; Elliot S.M. Oh
  3. Strategic Default in Financial Networks By Nizar Allouch; Maya Jalloul
  4. A Variational Approach to Network Games By Emerson Melo
  5. The Strength of Weak Leaders - An Experiment on Social Influence and Social Learning in Teams By Berno Büchel; Stefan Klößner; Martin Lochmüller; Heiko Rauhut
  6. Identification and Estimation of Large Network Games with Private Link Information By Hulya Eraslan; Xun Tang

  1. By: Marco Battaglini; Valerio Leone Sciabolazza; Eleonora Patacchini
    Abstract: In this paper, we study the extent to which social connections influence the legislative effectiveness of members of the U.S. Congress. We propose a new model of legislative effectiveness that formalizes the role of social connections and generates simple testable predictions. The model predicts that a legislator's equilibrium effectiveness is proportional to a specific weighted Katz-Bonacich centrality in the network of social connections, where the weights depend on the legislators' characteristics. We then propose a new empirical strategy to test the theoretical predictions using the network of cosponsorship links in the 109th-113th Congresses. The strategy addresses network endogeneity by implementing a two-step Heckman correction based on an original instrument: the legislators' alumni connections. We find that, in the absence of a correction, all measures of centrality in the cosponsorship network are significant. When we control for network endogeneity, however, only the measure suggested by the model remains significant, and the fit of the estimation is improved. We also study the influence of legislators' characteristics on the size of network effects. In doing so, we provide new insights into how social connectedness interacts with factors such as seniority, partisanship and legislative leadership in determining legislators' effectiveness.
    JEL: D72 D85
    Date: 2018–03
  2. By: Charles W. Calomiris; Elliot S.M. Oh
    Abstract: We study factors influencing individuals’ decisions to purchase Citibank stock during the 1920s. Citibank stock had a very high price per share and was only an investment option for wealthy people. The willingness to own shares was encouraged by proximity to New York, but constraints related to wealth and location were not the only barriers to stockholding. Lack of familiarity was also a barrier. Only a tiny fraction of the wealthy business elite living in the New York City metropolitan area owned Citibank shares. Individual characteristics related to wealth, knowledge, and one’s influence within the New York City business network increased the probability of becoming a Citibank shareholder. Those factors became less important during the stock price boom after 1927, and highly influential people became less likely than others to purchase Citibank shares during the price boom. Network connections were an important influence on purchase decisions. Having business connections with Citibank officers and directors, or with people who had such connections, increased the probability of buying Citibank shares. Furthermore, having business connections with other Citibank shareholders also increased the probability of buying Citibank shares. Thus network influence reflected more than the transmission of inside information; executives imitated each other’s stock buying behavior, which provides further evidence of the importance of familiarity for purchases. The role of network influences, like other identifiable influences, became less important during the price boom after 1927, likely reflecting the rising importance of other means of increasing familiarity during the price boom (i.e., media coverage).
    JEL: G02 G11 G21 N12 N22
    Date: 2018–03
  3. By: Nizar Allouch (University of Kent); Maya Jalloul (Queen Mary University of London)
    Abstract: This paper investigates a model of strategic interactions in financial networks, where the decision by one agent on whether or not to default impacts the incentives of other agents to escape default. Agents' payoffs are determined by the clearing mechanism introduced in the seminal contribution of Eisenberg and Noe (2001). We first show the existence of a Nash equilibrium of this default game. Next, we develop an algorithm to find all Nash equilibria that relies on the financial network structure. Finally, we explore some policy implications to achieve efficient coordination.
    Keywords: Systemic risk, default, financial networks, coordination games, central clearing, counterparty, financial regulation
    JEL: C72 D53 D85 G21 G28 G33
    Date: 2018–02–06
  4. By: Emerson Melo (Indiana University, Department of Economics)
    Abstract: This paper studies strategic interaction in networks. We focus on games of strategic substitutes and strategic complements, and departing from previous literature, we do not assume particular functional forms on players' payoffs. By exploiting variational methods, we show that the uniqueness, the comparative statics, and the approximation of a Nash equilibrium are determined by a precise relationship between the lowest eigenvalue of the network, a measure of players' payoff concavity, and a parameter capturing the strength of the strategic interaction among players. We apply our framework to the study of aggregative network games, games of mixed interactions, and Bayesian network games.
    Keywords: Network Games, Variational Inequalities, Lowest Eigenvalue, Shock Propagation
    JEL: C72 D85 H41 C61 C62
    Date: 2018–02
  5. By: Berno Büchel (University of Fribourg, Economics); Stefan Klößner (Saarland University, Statistics and Econometrics); Martin Lochmüller (Saarland University, Statistics and Econometrics); Heiko Rauhut (University of Zurich, Sociology)
    Abstract: We investigate how the selection process of a leader affects team performance with respect to social learning. We use a lab experiment in which an incentivized guessing task is repeated in a star network with the leader at the center. Leader selection is either based on competence, on self-confidence, or made at random. Teams with random leaders do not underperform compared to competent leaders, and they even outperform teams whose leader is selected based on self-confidence. The reason is that random leaders are better able to use the knowledge within the team. We can show that it is the declaration of the selection procedure which makes non-random leaders overly influential. We set up a horse race between several rational and naïve models of social learning to investigate the micro-level mechanisms. We find that overconfidence and conservatism contribute to the fact that overly influential leaders mislead their team.
    Keywords: Social Networks, Social Influence, Confidence, Overconfidence, Bayesian Updating, Naïve Learning, Sortition, Wisdom of Crowds
    JEL: D83 D85 C91
    Date: 2018–02
  6. By: Hulya Eraslan (Rice University, Department of Economics); Xun Tang (Rice University, Department of Economics)
    Abstract: We study the identification and estimation of large network games where each individual holds private information about its links and payoffs. Extending Galeotti, Goyal, Jackson, Vega-Redondo and Yariv (2010), we build a tractable empirical model of network games where the individuals are heterogeneous with private link and payoff information, and characterize its unique, symmetric pure-strategy Bayesian Nash equilibrium. We then show that the parameters in individual payoffs are identified under "large market" asymptotics, whereby the number of individuals increases to infinity in a fixed and small number of networks. We also propose a consistent two-step m-estimator for individual payoffs. Our method is distribution-free in that it does not require parametrization of the distribution of shocks in individual payoffs. Monte Carlo simulation show that our estimator has good performance in moderate-sized samples.
    Date: 2018–03

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